rnd-v1

scripts/run_pilot.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+# Run from repository root.
+# First run downloads two sheets only. Increase --limit after confirming overlays look sane.
+
+python -m bgtopo_poc.cli inventory \
+  --config configs/blue_detector.yaml \
+  --out data/manifest.csv \
+  --limit 2
+
+python -m bgtopo_poc.cli download \
+  --manifest data/manifest.csv \
+  --out-dir data/raw \
+  --out-manifest data/manifest_downloaded.csv \
+  --limit 2
+
+python - <<'PY'
+import pandas as pd
+from pathlib import Path
+m = pd.read_csv('data/manifest_downloaded.csv')
+for _, r in m.iterrows():
+    sid = r['sheet_id']
+    Path('data/interim/candidates').mkdir(parents=True, exist_ok=True)
+    print(f"Scanning {sid}")
+PY
+
+while IFS=, read -r sheet_id map_url tif_url map_path tif_path; do
+  if [[ "$sheet_id" == "sheet_id" ]]; then continue; fi
+  python -m bgtopo_poc.cli detect \
+    --config configs/blue_detector.yaml \
+    --sheet-id "$sheet_id" \
+    --map "$map_path" \
+    --tif "$tif_path" \
+    --out-dir data/interim/candidates
+
+  python -m bgtopo_poc.cli overlay \
+    --tif "$tif_path" \
+    --candidates "data/interim/candidates/${sheet_id}_candidates.csv" \
+    --out "reports/overlays/${sheet_id}_overlay.png"
+
+done < data/manifest_downloaded.csv
+
+python -m bgtopo_poc.cli report \
+  --candidates data/interim/candidates/*_candidates.csv \
+  --overlays reports/overlays/*_overlay.png \
+  --out reports/poc_report.html
+
+echo "Open reports/poc_report.html"

scripts/run_training_example.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+# Pick one downloaded sheet/candidate file first.
+SHEET_ID="${1:-K-34-009-2}"
+TIF="data/raw/${SHEET_ID}/${SHEET_ID}.tif"
+CAND="data/interim/candidates/${SHEET_ID}_candidates.csv"
+OUT="data/yolo/${SHEET_ID}"
+
+python -m bgtopo_poc.cli export-yolo \
+  --config configs/blue_detector.yaml \
+  --sheet-id "$SHEET_ID" \
+  --tif "$TIF" \
+  --candidates "$CAND" \
+  --out-dir "$OUT" \
+  --tile-size 1024 \
+  --overlap 128
+
+# RTX 3080 FE 10 GB: start batch 2-4 at imgsz=1024. Raise only if VRAM allows it.
+python -m bgtopo_poc.cli train-yolo \
+  --data-yaml "$OUT/data.yaml" \
+  --model yolov8s.pt \
+  --imgsz 1024 \
+  --epochs 80 \
+  --batch 4 \
+  --device 0